The Similarity Approach to EEG Analysis - Stassen, H. H. (Methods of Information in Medicine, 1985)
A method of approach to EEG analysis has been proposed which is based upon the concept of similarity. It especially aims at the analysis of the between-subject and within-subject similarity of EEG spectral patterns by means of adaptive procedures. Repeated measurements from the same individual were used as design samples, and new registrations after 14 days were referred to as test samples in order to determine the calibration parameters of the approach. The discrimination between the distributions of between-subject and within-subject similarity coefficients served as the criterion for the optimization algorithm. The optimization was terminated when the discriminating power reached 92% for both design samples and test samples. Accordingly, EEG spectral patterns were found to measure the distinct individuality of the EEG with good precision and to be stable over time.Consequently, a detailed analysis of the between-subject similarities by evaluating the fine graduations of mutual differences was of special interest. The realization of this idea led to a methodological problem because all basic information existed only in the form of non-metric similarity matrices. The problem was solved by means of a non-metric MDS which led to a geometric representation of the relational data. The final configuration of image points suggested a partitioning into 4 natural groupings comprising about ¾ of the samples. The remaining measurements were either isolated elements or formed smaller clusters whose relevance remained undecided. Contrary to traditional EEG classifications, this approach provided not only abstract clusters but also the positions of patterns within clusters and the relative positions of clusters to each other. The latter seems to be important in connection with the analysis of reactive changes in the human EEG, particularly because other investigations have shown that the effects of centrally acting substances are highly dependent upon the specific type of EEG.
Keywords: EEG; Individuality; Genetic Determination; Classification